Paper-qa is a retrieval augmented generation system designed for question answering and analysis of scientific literature and technical documents. It functions as an LLM-powered research assistant that extracts grounded answers and summaries with citations from a document library.
The system utilizes an agentic RAG orchestrator to iteratively refine search queries and gather evidence through multi-step tool calling. It features a multimodal document parser that extracts text, tables, and images from PDFs, alongside a vector-based indexer that embeds and caches document libraries for efficient semantic search.
The project covers a broad range of capabilities including contradiction detection across multiple papers, automated bibliographic metadata retrieval, and the ability to integrate with locally hosted language models. It manages the end-to-end workflow from multi-format document ingestion to two-stage vector retrieval and grounded answer generation.
The system includes configuration options for provider-agnostic model routing, prompt template customization, and rate limit management for API interactions.
